Charles Herbert Robert

Protein filaments are dynamic entities that respond to external stimuli by slightly or substantially modifying the internal binding geometries between successive protomers. This results in overall changes in the filament architecture, which are difficult to model due to the helical character of the system. Here, we describe how distortions in RecA nucleofilaments and their consequences on the filament-DNA and bound DNA-DNA interactions at different stages of the homologous recombination process can be modeled using the PTools/Heligeom software and subsequent molecular dynamics simulation with NAMD. Modeling methods dealing with helical macromolecular objects typically rely on symmetric assemblies and take advantage of known symmetry descriptors. Other methods dealing with single objects, such as MMTK or VMD, do not integrate the specificities of regular assemblies. By basing the model building on binding geometries at the protomer-protomer level, PTools/Heligeom frees the building process from a priori knowledge of the system topology and enables irregular architectures and symmetry disruption to be accounted for.Graphical Model of ATP hydrolysis-induced distortions in the recombinant nucleoprotein, obtained by combining RecA-DNA and two RecA-RecA binding geometries. Bio-protocol is an online peer-reviewed protocol journal. Its mission is to make life science research more efficient and reproducible by curating and hosting high quality, free access protocols.

Although they play a significant part in the regulation of microtubule structure, dynamics, and function, the disordered C-terminal tails of tubulin remain invisible to experimental structural methods and do not appear in the crystallographic structures that are currently available in the Protein Data Bank. Interestingly, these tails concentrate most of the sequence variability between tubulin isotypes and are the sites of the principal post-translational modifications undergone by this protein. Using homology modeling, we developed two complete models for the human αI/βI- and αI/βIII-tubulin isotypes that include their C-terminal tails. We then investigated the conformational variability of the two β-tails using long time-scale classical molecular dynamics simulations that revealed similar features, notably the unexpected presence of common anchoring regions on the surface of the tuulin dimer, but also distinctive mobility or interaction patterns, some of which could be related to the tail lengths and charge distributions. We also observed in our simulations that the C-terminal tail from the βI isotype, but not the βIII isotype, formed contacts in the putative binding site of a recently discovered peptide that disrupts microtubule formation in glioma cells. Hindering the binding site in the βI isotype would be consistent with this peptide’s preferential disruption of microtubule formation in glioma, whose cells overexpress βIII, compared to normal glial cells. While these observations need to be confirmed with more intensive sampling, our study opens new perspectives for the development of isotype-specific chemotherapy drugs.

The number of local minima of the potential energy landscape (PEL) of molecular systems generally grows exponentially with the number of degrees of freedom, so that a crucial property of PEL exploration algorithms is their ability to identify local minima, which are low lying and diverse. In this work, we present a new exploration algorithm, retaining the ability of basin hopping (BH) to identify local minima, and that of transition based rapidly exploring random trees (T‐RRT) to foster the exploration of yet unexplored regions. This ability is obtained by interleaving calls to the extension procedures of BH and T‐RRT, and we show tuning the balance between these two types of calls allows the algorithm to focus on low lying regions. Computational efficiency is obtained using state‐of‐the art data structures, in particular for searching approximate nearest neighbors in metric spaces. We present results for the BLN69, a protein model whose conformational space has dimension 207 and whose PEL has been studied exhaustively. On this system, we show that the propensity of our algorithm to explore low lying regions of the landscape significantly outperforms those of BH and T‐RRT.

Protein interactions are essential in all biological processes. The changes brought about in the structure when a free component forms a complex with another molecule need to be characterized for a proper understanding of molecular recognition as well as for the successful implementation of docking algorithms. Here, unbound (U) and bound (B) forms of protein structures from the Protein–Protein Interaction Affinity Database are compared in order to enumerate the changes that occur at the interface atoms/residues in terms of the solvent-accessible surface area (ASA), secondary structure, temperature factors (Bfactors) and disorder-to-order transitions. It is found that the interface atoms optimize contacts with the atoms in the partner protein, which leads to an increase in their ASA in the bound interface in the majority (69%) of the proteins when compared with the unbound interface, and this is independent of the root-mean-square deviation between the U and B forms. Changes in secondary structure during the transition indicate a likely extension of helices and strands at the expense of turns and coils. A reduction in flexibility during complex formation is reflected in the decrease inBfactors of the interface residues on going from the U form to the B form. There is, however, no distinction in flexibility between the interface and the surface in the monomeric structure, thereby highlighting the potential problem of usingBfactors for the prediction of binding sites in the unbound form for docking another protein. 16% of the proteins have missing (disordered) residues in the U form which are observed (ordered) in the B form, mostly with an irregular conformation; the data set also shows differences in the composition of interface and non-interface residues in the disordered polypeptide segments as well as differences in their surface burial.

NFL-TBS.40–63 is a 24 amino acid peptide corresponding to the tubulin-binding site located on the light neurofilament subunit, which selectively enters glioblastoma cells, where it disrupts their microtubule network and inhibits their proliferation. We investigated its structural variability and binding modes on a tubulin heterodimer using a combination of NMR experiments, docking, and molecular dynamics (MD) simulations. Our results show that, while lacking a stable structure, the peptide preferentially binds on a specific single site located near the β-tubulin C-terminal end, thus giving us precious hints regarding the mechanism of action of the NFL-TBS.40–63 peptide’s antimitotic activity at the molecular level.

We present novel algorithms and software addressing four core problems in computational structural biology, namely analyzing a conformational ensemble, comparing two conformational ensembles, analyzing a sampled energy landscape, and comparing two sampled energy landscapes. Using recent developments in computational topology, graph theory, and combinatorial optimization, we make two notable contributions. First, we present a generic algorithm analyzing height fields. We then use this algorithm to perform density‐based clustering of conformations, and to analyze a sampled energy landscape in terms of basins and transitions between them. In both cases, topological persistence is used to manage (geometric) frustration. Second, we introduce two algorithms to compare transition graphs. The first is the classical earth mover distance metric which depends only on local minimum energy configurations along with their statistical weights, while the second incorporates topological constraints inherent to conformational transitions. Illustrations are provided on a simplified protein model (BLN69), whose frustrated potential energy landscape has been thoroughly studied. The software implementing our tools is also made available, and should prove valuable wherever conformational ensembles and energy landscapes are used.

Oligomeric macromolecules in the cell self-organize into a wide variety of geometrical motifs such as helices, rings or linear filaments. The recombinase proteins involved in homologous recombination present many such assembly motifs. Here, we examine in particular the polymorphic characteristics of RecA, the most studied member of the recombinase family, using an integrative approach that relates local modes of monomer/monomer association to the global architecture of their screw-type organization. In our approach, local modes of association are sampled via docking or Monte Carlo simulations. This enables shedding new light on fiber morphologies that may be adopted by the RecA protein. Two distinct RecA helical morphologies, the so-called “extended” and “compressed” forms, are known to play a role in homologous recombination. We investigate the variability within each form in terms of helical parameters and steric accessibility. We also address possible helical discontinuities in RecA filaments due to multiple monomer-monomer association modes. By relating local interface organization to global filament morphology, the strategies developed here to study RecA self-assembly are particularly well suited to other DNA-binding proteins and to filamentous protein assemblies in general.

The buried surface area (BSA), which measures the size of the interface in a protein–protein complex may differ from the accessible surface area (ASA) lost upon association (which we call DSA), if conformation changes take place. To evaluate the DSA, we measure the ASA of the interface atoms in the bound and unbound states of the components of 144 protein–protein complexes taken from the Protein–Protein Interaction Affinity Database of Kastritis et al. (2011). We observe differences exceeding 20%, and a systematic bias in the distribution. On average, the ASA calculated in the bound state of the components is 3.3% greater than in their unbound state, and the BSA, 7% greater than the DSA. The bias is observed even in complexes where the conformation changes are small. An examination of the bound and unbound structures points to a possible origin: local movements optimize contacts with the other component at the cost of internal contacts, and presumably also the binding free energy.